Features, benefits, and challenges of document databases

A document database (or document-oriented database) is a type of NoSQL database designed to store and retrieve data as self-contained documents rather than as rows in relational tables. These documents are typically JSON or a JSON-compatible representations such as BSON. Some systems also support XML, containing key-value pairs that represent data about an object or entity. Unlike a relational database, where the schema or table structure must be predefined and consistent for all records, a document database allows each record or document to have its own structure. This means a document may contain different fields from another within the same collection, making schemas more flexible. 

Document databases are an extension of the key-value store concept. Each document is stored and retrieved via a unique key, often an ID, but the database also understands the internal structure of the document. This supports querying based on fields within the document, not just by key. 

In other words, where a pure key-value database would fetch a record only by its key, a document database returns projections of specific fields or subdocuments from within a JSON document. This makes document stores powerful for developers, because the stored data follows the JSON or object structures used in application code, avoiding the need for complex object-relational mapping layers.

By co-locating an entity’s data in one document, document databases often eliminate the need for relational JOINs for that access pattern. For example, instead of having separate tables for customers and their orders linked by keys, a document database might store a customer’s profile and an array of their orders in one nested JSON document. This self-contained approach makes data retrieval and update operations simpler. 

How document databases work

Document databases organize data in a hierarchical structure of databases, collections, and documents. In this context, a database is a logical grouping of data, similar to a database in relational systems. Within each database, documents are grouped into collections, which are analogous to tables in a relational database, except they don’t enforce a fixed schema. Each document in a collection is typically a JSON object containing fields and values that describe one real-world item or object. Documents in the same collection may have different fields, reflecting application-defined and optionally validated schema rather than a centrally enforced table schema.

Under the hood, a document database stores each JSON document as a value associated with a unique key. The engine indexes documents by the primary key and may support secondary indexes on nested paths, arrays, text, or geo fields for fast lookups. Querying a document store lets you retrieve entire documents or specific elements within documents using a query language or syntax, such as JSON-based query filters. Many document databases provide rich query capabilities with path expressions and JSON-style predicates that match nested fields and array elements. This integrated data retrieval lets developers query complex data structures such as arrays and nested objects directly, without needing to flatten or join data from multiple sources.

Each document is typically self-contained, which reduces the need for cross-node coordination when fulfilling queries. They often distribute data across multiple nodes in a cluster by sharding to support adding more servers to share the load as your data or traffic grows. 

For example, if data is partitioned by a user ID, all of a given user’s information might reside on one shard as one document, so reads and writes for that user are served by one node without involving others. This design helps keep throughput high and latency low as data volumes increase.

Document databases do not require a centralized schema, but many support schema validation rules and type constraints when needed. Some systems offer schema validation rules or schemas at the application level, so certain fields or data types are present in documents. This provides flexibility for developers during iteration, while still allowing organizations to impose governance or data quality checks for important collections.

Advantages of document databases

Document databases rose in popularity as applications needed more flexibility and scalability than traditional relational databases could easily provide. They offer several benefits:

Flexible schema design

The foremost advantage of document databases is their flexible schemas. You don’t have to define all possible fields up front or perform costly schema migrations when requirements change. Documents introduce new fields on the fly or omit unneeded fields from other documents. This flexibility helps in fast-paced development environments where the data model evolves with new features. Applications adapt quickly because adding a new attribute to a JSON document doesn’t break the database or require altering existing records. The document model lets the data structure evolve alongside the application easily. 

Horizontal scalability

Document databases are designed for a distributed, horizontally-scaled architecture. They partition or shard data across multiple servers and handle traffic and data growth by adding more nodes. This scaling out lets a document store manage large datasets and high throughput while maintaining performance. 

For organizations dealing with web-scale applications or big data, this is an advantage. Scaling a relational database often requires expensive hardware upgrades or complex sharding at the application level, while document databases build sharding into their core. Most provide synchronous or asynchronous replication and configurable read/write concerns that trade latency for durability and consistency. This typically doesn’t require much downtime and handles growth more gracefully.

Faster development and iteration

Because document databases use JSON or similar documents, they align with the data formats used in today’s applications, such as JavaScript and Python dictionaries. This makes development more intuitive. Developers store objects directly from their code into the database and retrieve them with less need for object-relational mappers or complex SQL. 

As a result, teams iterate faster because they implement new features that require storing additional data, just by saving that data in the document. There’s less upfront modeling needed, which is especially useful in agile development or when requirements are not fully known ahead of time. Adjusting the data model helps businesses respond to changing needs faster. 

Unified data for each entity

One document encapsulates an entire entity’s information, including related sub-entities, in one place. This makes reads faster and simpler for entity-centric access patterns; cross-entity analytics may still require pipelines or external engines. 

For instance, an e-commerce product document might contain not just product details but also an array of reviews. Retrieving that one document provides both the product info and the reviews together, which is convenient for the application and reduces the number of database operations. This locality of related data leads to performance benefits and simpler code, as there’s no need to assemble data from multiple sources. It’s particularly powerful for data that is frequently accessed together.

Moreover, the embedded data approach supports optimizations for read-heavy workloads. Because duplication of some data is allowed, such as storing an author's name inside each of their articles’ documents, read queries don’t have to join or look up that data elsewhere; it’s already present in each document. This tradeoff of disk space for speed often pays off in scenarios such as caching or content management that need quick reads. 

Geographical distribution and availability

Today’s document databases often support multiple data centers or multiple regions. Because of their distributed nature, they support deployment across data centers around the world, keeping data close to users and improving access times globally. Many provide features such as automatic replication and geo-partitioning of data, which not only improve read latency for distributed users but also make the system more resilient, because if one region goes down, another region provides the data. Document stores frequently prioritize availability and partition tolerance, meaning they are designed to keep the system running and accept data in the face of network partitions, potentially at the expense of immediate consistency. The bottom line is that document databases are a natural fit for cloud and distributed environments, providing high availability and low latency across global user bases.

Cost efficiency at scale

The schema-less, horizontal scale nature of document databases also translates to cost savings for large systems. They are typically built to run on clusters of commodity hardware or cloud instances, rather than requiring specialized, high-end servers. As data grows, organizations add capacity incrementally, or even automatically in some managed services, instead of overhauling a single big machine. 

Additionally, because they often handle both storage and caching roles by allowing denormalization and embedding of data, document databases reduce the need for separate caching layers or multiple databases for fast access patterns. This consolidation simplifies the architecture and requires fewer servers. 

Some case studies have shown that using a high-performance document-oriented or key-value store in place of a larger cluster of a slower database reduces the number of servers needed, lowering operational costs. In short, with the right design, document databases offer better price/performance at scale, doing more with less hardware.

Challenges and limitations of document databases

While document databases are flexible, they also come with certain tradeoffs and challenges. It’s important to be aware of these limitations when deciding whether a document model is the right fit for your project:

Data consistency and transaction support

Many document databases let you choose consistency, whether eventual, causal, or strong, via read/write settings; stricter guarantees typically add latency. This may work for situations where slightly outdated data is tolerable, but it’s problematic for scenarios that require immediate consistency across reads. 

Additionally, transactional support spanning multiple documents is limited in some document databases. Traditional relational systems excel at multi-record transactions, supporting atomic updates to several tables or rows, while document databases historically focused on single-document transactions. 

Today’s implementations, such as newer versions of MongoDB and Aerospike 8.0, support multi-document ACID transactions, but using them may reduce performance. In general, if your application requires complex transactions touching many pieces of data, such as a banking system transferring money between accounts, a document database might not be the ideal choice, or you must use those features knowing the performance costs.

Tuning a document database for the right balance of consistency and availability is important. Options to configure write concerns, read preferences, or use certain topologies provide stronger consistency, but at the risk of availability or speed. This CAP theorem tradeoff must be managed based on application needs.

Data redundancy and update complexity

The freedom to embed related data in documents, or denormalization, creates its own problems. Data redundancy may creep in. 

For example, if an author’s name is stored in each of their blog post documents, that name is duplicated multiple times. This isn’t inherently bad because it speeds reads, but it complicates updates. If the author changes their name, the database must update that field in each of their documents, or the data becomes inconsistent. There is no automatic mechanism to propagate such changes as there would be with a normalized relational schema. Developers and data architects need to design what to embed as opposed to what to reference to avoid maintenance issues. Inconsistencies and anomalies arise if redundant data isn’t managed correctly. 

Essentially, with great flexibility comes responsibility. The onus is on the application or database design to maintain data integrity when redundancy exists.

Limited querying and analytics capabilities

Document databases typically have powerful query capabilities within a document, and for simple filters across documents, but they may not match the full breadth of SQL in relational databases for complex analytics. 

For example, performing ad hoc aggregations, multi-stage joins across different collections, or reporting queries is more challenging. Many document stores lack native relational joins; some offer lookup/merge operators within a pipeline, but cross-collection joins remain limited. Some have introduced aggregation frameworks and SQL-like query languages or connectors, but there can still be a gap for certain use cases. 

If you need to frequently perform heavy-duty analytics, group-bys, or combine data across different datasets, a document database might require additional tooling or data pipelines to accomplish that, such as exporting data to a data warehouse or using map-reduce style processing. 

Additionally, each document database tends to have its own query language or API, which means a learning curve and potential lack of portability of skills. Developers familiar with SQL might find it difficult to do the same thing in a document query language, especially for complex queries.

Maturity of tooling and ecosystem

Relational databases have been around for decades, with an ecosystem of management and monitoring tools, proven patterns, and a large talent pool of experts. Document databases, while no longer “new,” are younger. Some document stores have a strong community and plenty of tools, but in general, the ecosystem for NoSQL is still catching up to the rich tooling of the SQL world. There may be fewer third-party integrations available, or fewer off-the-shelf solutions for challenges such as visualizing data, migrating schemas (because there is no fixed schema), or profiling query performance. 

That said, the gap has been closing as document databases get used more. Still, organizations might find that integrating a document database into an existing infrastructure requires more custom development or newer skill sets. Ensuring your team has expertise in the specific document database technology and understanding its operational quirks is important to avoid issues in production.

Operationally, running a large-scale document database cluster is complex. For example, sharding or horizontal partitioning is not always automatic; in some systems, choosing shard keys and managing cluster balance requires planning. Poor sharding strategy leads to “hotspots” of uneven load distribution or difficult rebalancing operations. 

Some document database users report that scaling out is more complex and may even require downtime during cluster reconfiguration. 

For instance, one common document database requires electing a primary node and performing chunk migrations when scaling, which interrupts service during those changes. This operational complexity is something to consider if you expect to add servers frequently or if you need near-zero downtime.

Schema evolution and data governance

Although “schema-less” is a selling point, it doesn’t mean “structure-less.” In practice, as an application evolves, you may end up with multiple versions of documents in your collection, where older documents have an old structure and newer ones have new fields. Managing this schema evolution over time is challenging. Without governance, a collection turns into a mix of document shapes that complicates query logic and application code. Teams often need to establish their own conventions and migration procedures to update older documents or to phase out deprecated fields. 

Unlike relational databases, where the schema is a contract and rejects out-of-spec data, a document database accepts anything, meaning responsibility for keeping the data model clean lies with the developers and data architects. Lack of an enforced schema also means fewer guarantees about data quality unless you implement validation. This flexibility requires discipline; otherwise, one could encounter bugs or unexpected results due to documents not having the expected structure or missing some fields.

In terms of governance and compliance, document databases can be used in regulated environments, but enforcing factors such as data type consistency, required fields, or relationships between data might require additional layers, such as application logic or using schema validation features if available. Keeping flexibility from violating business rules is an important part of using a document store in enterprises.

Common examples of document databases

Document databases are well-suited for a variety of uses, particularly those involving semi-structured data, changeable requirements, or large-scale user-facing applications. Here are some common scenarios where document databases shine.

Content management and catalogs

Document databases are a natural fit for content management systems, blogging platforms, news sites, and e-commerce catalogs. In these domains, each item of content or product listing might have a unique set of attributes. 

For example, an e-commerce site’s product catalog could use a document model where each product is a document containing all its details, including title, description, price, attributes, and reviews. Different products may have different attributes. 

For example, a book might have an author and an ISBN, while a smartphone has specs such as screen size and battery life. A relational model for this would either require many sparse columns or separate tables for each product type, which is complex and inefficient. A document database handles this variability easily: Each product’s JSON document simply contains the fields it needs. This makes retrieving a product and all its information at once efficient and flexible to add new attributes as products evolve.

Similarly, in content management, such as articles, blog posts, or videos, each piece of content and its metadata can be one document. If the structure of the content metadata changes, such as when you introduce a field for SEO tags, you can add it to new documents without having to alter a global schema or migrate old records; old content remains as is until updated. This is useful for media companies and websites that continuously change how they categorize or annotate content.

User profiles and personalization

Many applications maintain detailed profiles of users or customers, such as a gaming app storing player profiles, or a retail site storing customer preferences and history. Document databases work well for user profile data because each user’s data can be aggregated into one JSON document. One user might have a dozen preferences saved, another might have only two; one user might link three social accounts to their profile, others none. A document model accommodates this by having those fields present when needed. It’s also straightforward to store nested data such as arrays of addresses, lists of past orders, or settings. Reading a user’s entire profile is one fast document fetch, and updating needs just one operation as well.

Personalization systems, such as recommendation engines and personalized content feeds, also benefit here because they often store a dynamic set of signals or preferences for each user. A document database could store a user’s recent activity log, preferences, and recommended items in one record that evolves over time. This supports real-time personalization. As soon as something about the user changes, their document gets updated, and the next read reflects the new state. The flexible schema lets you add new types of personalization data without complex migrations.

IoT and sensor data

Internet of Things applications generate large amounts of semi-structured data from sensors, devices, and logs. Document databases capture and store this sensor data flexibly. Each sensor reading or device log is a JSON document that might contain various readings or attributes such as timestamp, device ID, location, and measurements. Internet of Things (IoT) data often comes in large volumes with missing values or slightly different schema from one device type to another. Document stores handle this by not requiring every record to align to the same columns.

For example, imagine a smart home system: Thermostats report temperature and humidity, motion sensors report movement and battery level, smart locks report lock/unlock events. Each of these is stored as documents in one collection or in multiple collections for each device type. If a new sensor type is added later, such as an air quality sensor with new metrics, its data starts being stored without any database schema change. 

In addition, document databases support horizontal scaling to handle many events. Keep raw records for a period of time for analysis, and use partial queries to extract just the needed fields for analytics or monitoring dashboards. Once older data ages out, entire documents can be archived or deleted. The hierarchical nature of JSON also supports storing nested data, such as a batch of readings in one document, if that’s convenient for certain queries.

Real-time analytics and caching layers

Certain real-time analytics use cases take advantage of document databases, especially when the application needs fast access to pre-aggregated or session-specific data. 

For instance, consider a dashboard that shows a user’s real-time statistics, such as in a gaming leaderboard or live finance app. The system might store a running summary document per user or per session that gets updated frequently. With a document store, updating a specific field or sub-document, such as incrementing a counter, is straightforward and doesn’t require rewriting the whole document, thanks to update-in-place operations supported by many document databases.

Document databases are also used as a generalized cache or a high-speed lookup store in front of slower systems. Because they store denormalized data, you might aggregate data from multiple sources into one JSON document and keep it in a document database for quick reads. This way, the application fetches one document to display a complex view, such as all of a user’s account information, including data from different services, without querying multiple systems. The document database in this scenario serves as a flexible caching layer that is updated as needed and is far more queryable than a simple key-value cache such as Memcached. 

In fact, some organizations start with a combination of a key-value cache and another database, and later consolidate into a document database that serves both roles, simplifying their stack. For example, an advertising technology company combined user profiles from MongoDB and a caching system into a unified Aerospike database for sub-millisecond reads.

Applications with rapidly evolving features

Any application in active development with frequently changing features benefits from the schema flexibility of a document database. Startups and agile development teams often choose a document model to add or deprecate data fields with each iteration of the product without spending time on migrations. 

For example, a social networking app adding new profile sections or new post types just starts storing the new data in documents. Older documents for users who haven’t updated to the new feature remain in the system and cause no issues; they simply lack the new fields. Over time, the app might backfill defaults or handle the absence of those fields in code. This flexibility makes development faster and lets data storage adapt organically as the product evolves.

It’s worth noting that while document databases fit these use cases well, they are not a panacea. Workloads that require complex, multi-entity transactions, such as banking systems, or those that involve heavy relational querying across many data items, such as certain business intelligence queries, might still favor relational databases or hybrid approaches. The good news is that the ecosystem now provides many choices, and there’s a trend toward multi-model databases that try to offer the best of both worlds.

Aerospike and the document database approach

Document databases clearly address many challenges of applications by providing flexibility and performance. 

However, not all document databases are equal when it comes to large workloads and real-time demands. This is where Aerospike comes into the picture. Aerospike is a high-performance NoSQL database platform known for its sub-millisecond latency and ability to handle billions of transactions per day. Aerospike has native support for JSON document data models, evolving from a pure key-value store into a multi-model database that includes document database capabilities. This lets you store and query JSON documents in Aerospike, gaining the schema flexibility of a document database on top of Aerospike’s proven strength in speed, scale, and reliability.

What does Aerospike bring to document data? Aerospike’s architecture provides consistent low latency and high throughput, which helps overcome challenges users might face with other document databases, such as unpredictable latencies or difficult scaling when data grows. 

For example, companies that struggled with other NoSQL document stores have migrated to Aerospike and seen improvements. One large e-commerce platform found that its previous document database’s response times had deteriorated from 5ms to more than one second, while Aerospike could restore read latencies to low milliseconds even under heavy load. 

Similarly, organizations have reduced cluster size and hardware costs by consolidating on Aerospike, achieving the same or better performance with a fraction of the servers, thanks to Aerospike’s efficiency.

Aerospike’s real-time data platform also addresses many problems by offering strong consistency options that eliminate the consistency versus availability dilemma for globally distributed data, automatic re-sharding and fault tolerance to keep the database “always on” without manual tuning during node failures, and a patented Hybrid Memory Architecture that reduces cost. Aerospike’s multi-model support means you don’t have to deploy one database for key-value use cases and another for document use cases; one Aerospike cluster handles both, simplifying your data infrastructure.

If your enterprise is looking to take advantage of the flexibility of JSON document data without sacrificing speed, scale, or reliability, Aerospike offers a compelling solution. Visit Aerospike’s website to learn more about how Aerospike’s document database capabilities future-proof your data architecture, or contact us to see how Aerospike powers your next-generation applications. 

With Aerospike, you get the best of both worlds: the agility of a document store and the performance of an enterprise-grade, real-time database.